Dan’s COVID Charts

Created by Dan Goodspeed
using data from New York Times and
visualization help from Flourish.

Some have asked for ways to donate to help keep the charts updated. It takes me about an hour a day to update the seven charts. I'm not expecting much, but if you'd like, I made a few options. I also made a monthly email newsletter with chart updates exclusively for supporters. Thanks! -Dan

Paypal | GoFundMe | Patreon

This chart is a little different than the others. It's an "impact index" score for each state factoring in cases, hospitalizations, and deaths. Having a chart that doesn't change as much from day to day (as each day only changes by 1/90th) allows the animation to run smoother and faster. And also, adding everything into one chart makes it easier to keep updated daily. I wanted a single metric that can best characterize the negative effect the virus is having on a population, factoring in the three metrics I have to work with- cases, deaths, and hospitalizations.

Hospitalizations are a different category in that 1) They're from covidtracking.com, where all my other data comes from NYT, and 2) It just gives me daily hospitalization rates. So it's totally likely that a person will be counted more than once if they're hospitalized more than a day, but the number still works well for a state-by-state comparison. What I ended up doing was averaging the (per million) daily normalized* deaths, hospitalizations, and cases for the given 90-day period... counting deaths three times and hospitalizations twice. The more severe the category, the more it's counted. The formula I'm using is this:

** Impact Index = (CASES + HOSPITALIZATIONS * 2 + DEATHS * 3) / 6
What you can take from the chart- When a number is going up, that state is doing worse than they did three months ago (going down means they're doing better). And you can also compare states. And you can divide the number by 10,000 to get a really rough estimate of the percentage of the population that is undergoing a serious, sometimes deadly case of COVID.

* "Normalization" (perhaps better called "smoothing") means the abnormalities in the data were evened out. For example, if there were 10 days in a row of a few cases/deaths a day and then one day of 1000... that looks awful and frenetic on a chart like this, even when framed in a per-week display. In reality, that 1000 is just a backlog catch-up, so I normalized it by spreading the thousand over previous dates for a more even / more realistic data. It works similarly when the total number of cases/deaths drops one day. Likely a correction from a previous report, I just subtracted the difference over previous dates to numbers that are probably closer to reality.